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Process & Analyze Bugzilla Data in Databricks (AWS)



Host the CData JDBC Driver for Bugzilla in AWS and use Databricks to perform data engineering and data science on live Bugzilla data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Bugzilla data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Bugzilla data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Bugzilla data. When you issue complex SQL queries to Bugzilla, the driver pushes supported SQL operations, like filters and aggregations, directly to Bugzilla and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Bugzilla data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Bugzilla data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.bugzilla.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Bugzilla\lib).

Access Bugzilla Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Bugzilla data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Bugzilla, and create a basic report.

Configure the Connection to Bugzilla

Connect to Bugzilla by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.bugzilla.BugzillaDriver"
url = "jdbc:bugzilla:RTK=5246...;Url=http://yourdomain/Bugzilla;APIKey=abc123;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Bugzilla JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.bugzilla.jar

Fill in the connection properties and copy the connection string to the clipboard.

You can authenticate to your Bugzilla account using two parameters:

  • URL: The URL of your Bugzilla developer's page (the Home page).
  • ApiKey: API Keys can be generated from the Preferences -> API Keys section of your Bugzilla developer's page.

Load Bugzilla Data

Once you configure the connection, you can load Bugzilla data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Bugs") \
	.load ()

Display Bugzilla Data

Check the loaded Bugzilla data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Bugzilla Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Bugzilla data for reporting, visualization, and analysis.

% sql

SELECT Id, Summary FROM SAMPLE_VIEW ORDER BY Summary DESC LIMIT 5

The data from Bugzilla is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Bugzilla and start working with your live Bugzilla data in Databricks. Reach out to our Support Team if you have any questions.